DocTr去扭曲

doc_dewarp/extractor.py

@@ -0,0 +1,110 @@
+import paddle.nn as nn
+
+from .weight_init import weight_init_
+
+
+class ResidualBlock(nn.Layer):
+    """Residual Block with custom normalization."""
+
+    def __init__(self, in_planes, planes, norm_fn="group", stride=1):
+        super(ResidualBlock, self).__init__()
+
+        self.conv1 = nn.Conv2D(in_planes, planes, 3, padding=1, stride=stride)
+        self.conv2 = nn.Conv2D(planes, planes, 3, padding=1)
+        self.relu = nn.ReLU()
+
+        if norm_fn == "group":
+            num_groups = planes // 8
+            self.norm1 = nn.GroupNorm(num_groups, planes)
+            self.norm2 = nn.GroupNorm(num_groups, planes)
+            if not stride == 1:
+                self.norm3 = nn.GroupNorm(num_groups, planes)
+        elif norm_fn == "batch":
+            self.norm1 = nn.BatchNorm2D(planes)
+            self.norm2 = nn.BatchNorm2D(planes)
+            if not stride == 1:
+                self.norm3 = nn.BatchNorm2D(planes)
+        elif norm_fn == "instance":
+            self.norm1 = nn.InstanceNorm2D(planes)
+            self.norm2 = nn.InstanceNorm2D(planes)
+            if not stride == 1:
+                self.norm3 = nn.InstanceNorm2D(planes)
+        elif norm_fn == "none":
+            self.norm1 = nn.Sequential()
+            self.norm2 = nn.Sequential()
+            if not stride == 1:
+                self.norm3 = nn.Sequential()
+
+        if stride == 1:
+            self.downsample = None
+        else:
+            self.downsample = nn.Sequential(
+                nn.Conv2D(in_planes, planes, 1, stride=stride), self.norm3
+            )
+
+    def forward(self, x):
+        y = x
+        y = self.relu(self.norm1(self.conv1(y)))
+        y = self.relu(self.norm2(self.conv2(y)))
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        return self.relu(x + y)
+
+
+class BasicEncoder(nn.Layer):
+    """Basic Encoder with custom normalization."""
+
+    def __init__(self, output_dim=128, norm_fn="batch"):
+        super(BasicEncoder, self).__init__()
+
+        self.norm_fn = norm_fn
+
+        if self.norm_fn == "group":
+            self.norm1 = nn.GroupNorm(8, 64)
+        elif self.norm_fn == "batch":
+            self.norm1 = nn.BatchNorm2D(64)
+        elif self.norm_fn == "instance":
+            self.norm1 = nn.InstanceNorm2D(64)
+        elif self.norm_fn == "none":
+            self.norm1 = nn.Sequential()
+
+        self.conv1 = nn.Conv2D(3, 64, 7, stride=2, padding=3)
+        self.relu1 = nn.ReLU()
+
+        self.in_planes = 64
+        self.layer1 = self._make_layer(64, stride=1)
+        self.layer2 = self._make_layer(128, stride=2)
+        self.layer3 = self._make_layer(192, stride=2)
+
+        self.conv2 = nn.Conv2D(192, output_dim, 1)
+
+        for m in self.sublayers():
+            if isinstance(m, nn.Conv2D):
+                weight_init_(
+                    m.weight, "kaiming_normal_", mode="fan_out", nonlinearity="relu"
+                )
+            elif isinstance(m, (nn.BatchNorm2D, nn.InstanceNorm2D, nn.GroupNorm)):
+                weight_init_(m, "Constant", value=1, bias_value=0.0)
+
+    def _make_layer(self, dim, stride=1):
+        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
+        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
+        layers = layer1, layer2
+
+        self.in_planes = dim
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu1(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+
+        x = self.conv2(x)
+
+        return x